Surgical Process Models Research Articles

An ontology-based tool for modeling and documenting events in neurosurgery

BackgroundIntraoperative neurophysiological monitoring (IOM) plays a pivotal role in enhancing patient safety during neurosurgical procedures. This vital technique involves the continuous measurement of evoked potentials to provide early warnings and ensure the preservation of critical neural structures. One of the primary challenges has been the effective documentation of IOM events with semantically enriched characterizations. This study aimed to address this challenge by developing an ontology-based tool.MethodsWe structured the development of the IOM Documentation Ontology (IOMDO) and the associated tool into three distinct phases. The initial phase focused on the ontology’s creation, drawing from the OBO (Open Biological and Biomedical Ontology) principles. The subsequent phase involved agile software development, a flexible approach to encapsulate the diverse requirements and swiftly produce a prototype. The last phase entailed practical evaluation within real-world documentation settings. This crucial stage enabled us to gather firsthand insights, assessing the tool’s functionality and efficacy. The observations made during this phase formed the basis for essential adjustments to ensure the tool’s productive utilization.ResultsThe core entities of the ontology revolve around central aspects of IOM, including measurements characterized by timestamp, type, values, and location. Concepts and terms of several ontologies were integrated into IOMDO, e.g., the Foundation Model of Anatomy (FMA), the Human Phenotype Ontology (HPO) and the ontology for surgical process models (OntoSPM) related to general surgical terms. The software tool developed for extending the ontology and the associated knowledge base was built with JavaFX for the user-friendly frontend and Apache Jena for the robust backend. The tool’s evaluation involved test users who unanimously found the interface accessible and usable, even for those without extensive technical expertise.ConclusionsThrough the establishment of a structured and standardized framework for characterizing IOM events, our ontology-based tool holds the potential to enhance the quality of documentation, benefiting patient care by improving the foundation for informed decision-making. Furthermore, researchers can leverage the semantically enriched data to identify trends, patterns, and areas for surgical practice enhancement. To optimize documentation through ontology-based approaches, it’s crucial to address potential modeling issues that are associated with the Ontology of Adverse Events.

A surgical activity model of laparoscopic cholecystectomy for co-operation with collaborative robots

BackgroundLaparoscopic cholecystectomy is a very frequent surgical procedure. However, in an ageing society, less surgical staff will need to perform surgery on patients. Collaborative surgical robots (cobots) could address surgical staff shortages and workload. To achieve context-awareness for surgeon-robot collaboration, the intraoperative action workflow recognition is a key challenge.MethodsA surgical process model was developed for intraoperative surgical activities including actor, instrument, action and target in laparoscopic cholecystectomy (excluding camera guidance). These activities, as well as instrument presence and surgical phases were annotated in videos of laparoscopic cholecystectomy performed on human patients (n = 10) and on explanted porcine livers (n = 10). The machine learning algorithm Distilled-Swin was trained on our own annotated dataset and the CholecT45 dataset. The validation of the model was conducted using a fivefold cross-validation approach.ResultsIn total, 22,351 activities were annotated with a cumulative duration of 24.9 h of video segments. The machine learning algorithm trained and validated on our own dataset scored a mean average precision (mAP) of 25.7% and a top K = 5 accuracy of 85.3%. With training and validation on our dataset and CholecT45, the algorithm scored a mAP of 37.9%.ConclusionsAn activity model was developed and applied for the fine-granular annotation of laparoscopic cholecystectomies in two surgical settings. A machine recognition algorithm trained on our own annotated dataset and CholecT45 achieved a higher performance than training only on CholecT45 and can recognize frequently occurring activities well, but not infrequent activities.The analysis of an annotated dataset allowed for the quantification of the potential of collaborative surgical robots to address the workload of surgical staff. If collaborative surgical robots could grasp and hold tissue, up to 83.5% of the assistant’s tissue interacting tasks (i.e. excluding camera guidance) could be performed by robots.

The Sequence of Steps: A Key Concept Missing in Surgical Training-A Systematic Review and Recommendations to Include It.

Surgical procedures have an inherent feature, which is the sequence of steps. Moreover, studies have shown variability in surgeons' performances, which is valuable to expose residents to different ways to perform a procedure. However, it is unclear how to include the sequence of steps in training programs. We conducted a systematic review, including studies reporting explicit teaching of a standard sequence of steps, where assessment considered adherence to a standard sequence, and where faculty or students at any level participated. We searched for articles on PubMed, EMBASE, CINAHL, Web of Science, and Google Scholar databases. We selected nine articles that met the inclusion criteria. The main strategy to teach the sequence was to use videos to demonstrate the procedure. The simulation was the main strategy to assess the learning of the sequence of steps. Non-standardized scoring protocols and written tests with variable validity evidence were the instruments used to assess the learning, and were focused on adherence to a standard sequence and the omission of steps. Teaching and learning assessment of a standard sequence of steps is scarcely reported in procedural skills training literature. More research is needed to evaluate whether the new strategies to teach and assess the order of steps work. We recommend the use of Surgical Process Models and Surgical Data Science to incorporate the sequence of steps when teaching and assessing procedural skills.

ProDeM: A Process-Oriented Delphi Method for systematic asynchronous and consensual surgical process modelling

Surgical process models support improving healthcare provision by facilitating communication and reasoning about processes in the medical domain. Modelling surgical processes is challenging as it requires integrating information that might be fragmented, scattered, and not process-oriented. These challenges can be faced by involving healthcare domain experts during process modelling. This paper presents ProDeM: a novel Process-Oriented Delphi Method for the systematic, asynchronous, and consensual modelling of surgical processes. ProDeM is an adaptable and flexible method that acknowledges that: (i) domain experts have busy calendars and might be geographically dispersed, and (ii) various elements of the process model need to be assessed to ensure model quality. The contribution of the paper is twofold as it outlines ProDeM, but also demonstrates its operationalisation in the context of a well-known surgical process. Besides showing the method’s feasibility in practice, we also present an evaluation of the method by the experts involved in the demonstration.

Entrustability Meets Surgical Process Modeling: Case-Specific Prediction of Operative Autonomy

Entrustability Meets Surgical Process Modeling: Case-Specific Prediction of Operative Autonomy

Generic surgical process model for minimally invasive liver treatment methods

Surgical process modelling is an innovative approach that aims to simplify the challenges involved in improving surgeries through quantitative analysis of a well-established model of surgical activities. In this paper, surgical process model strategies are applied for the analysis of different Minimally Invasive Liver Treatments (MILTs), including ablation and surgical resection of the liver lesions. Moreover, a generic surgical process model for these differences in MILTs is introduced. The generic surgical process model was established at three different granularity levels. The generic process model, encompassing thirteen phases, was verified against videos of MILT procedures and interviews with surgeons. The established model covers all the surgical and interventional activities and the connections between them and provides a foundation for extensive quantitative analysis and simulations of MILT procedures for improving computer-assisted surgery systems, surgeon training and evaluation, surgeon guidance and planning systems and evaluation of new technologies.

Surgical declarative knowledge learning: concept and acceptability study

Improving surgical training by means of technology assistance is an important challenge that aims to directly impact surgical quality. Surgical training includes the acquisition of two categories of knowledge: declarative knowledge (i.e. ‘knowing what’) and procedural knowledge (i.e. ‘knowing how’). It is essential to acquire both before performing any particular surgery. There are currently many tools for acquiring procedural knowledge, such as simulators. However, few approaches or tools allow a trainer to formalize and record surgical declarative knowledge, and a trainee to have easy access to it. In this paper, we propose an approach for structuring surgical declarative knowledge according to procedural knowledge and based on surgical process modeling. A dedicated software application has been implemented. We evaluated the concept and the software usability on two procedures with different medical populations: endoscopic third ventriculostomy involving 6 neurosurgeons and preparation of a surgical table for craniotomy involving 4 scrub nurses. The results of both studies show that surgical process models could be a well-adapted approach for structuring and visualizing surgical declarative knowledge. The software application was perceived by neurosurgeons and scrub nurses as an innovative tool for managing and presenting surgical knowledge. The preliminary results show that the feasibility of the proposed approach and the acceptability and usability of the corresponding software. Future experiments will study impact of such an approach on knowledge acquisition.

Clinical study of skill assessment based on time sequential measurement changes

Endoscopic sinus surgery is a common procedure for chronic sinusitis; however, complications have been reported in some cases. Improving surgical outcomes requires an improvement in a surgeon’s skills. In this study, we used surgical workflow analysis to automatically extract “errors,” indicating whether there was a large difference in the comparative evaluation of procedures performed by experts and residents. First, we quantified surgical features using surgical log data, which contained surgical instrument information (e.g., tip position) and time stamp. Second, we created a surgical process model (SPM), which represents the temporal transition of the surgical features. Finally, we identified technical issues by creating an expert standard SPM and comparing it to the novice SPM. We verified the performance of our methods by using the clinical data of 39 patients. In total, 303 portions were detected as an error, and they were classified into six categories. Three risky operations were overlooked, and there were 11 overdetected errors. We noted that most errors detected by our method involved dangers. The implementation of our methods of automatic improvement points detection may be advantageous. Our methods may help reduce the time for reviewing and improving the surgical technique efficiently.

Surgical Process Modeling for Open Spinal Surgeries.

Modern operating rooms are becoming increasingly advanced thanks to the emerging medical technologies and cutting-edge surgical techniques. Current surgeries are transitioning into complex processes that involve information and actions from multiple resources. When designing context-aware medical technologies for a given intervention, it is of utmost importance to have a deep understanding of the underlying surgical process. This is essential to develop technologies that can correctly address the clinical needs and can adapt to the existing workflow. Surgical Process Modeling (SPM) is a relatively recent discipline that focuses on achieving a profound understanding of the surgical workflow and providing a model that explains the elements of a given surgery as well as their sequence and hierarchy, both in quantitative and qualitative manner. To date, a significant body of work has been dedicated to the development of comprehensive SPMs for minimally invasive baroscopic and endoscopic surgeries, while such models are missing for open spinal surgeries. In this paper, we provide SPMs common open spinal interventions in orthopedics. Direct video observations of surgeries conducted in our institution were used to derive temporal and transitional information about the surgical activities. This information was later used to develop detailed SPMs that modeled different primary surgical steps and highlighted the frequency of transitions between the surgical activities made within each step. Given the recent emersion of advanced techniques that are tailored to open spinal surgeries (e.g., artificial intelligence methods for intraoperative guidance and navigation), we believe that the SPMs provided in this study can serve as the basis for further advancement of next-generation algorithms dedicated to open spinal interventions that require a profound understanding of the surgical workflow (e.g., automatic surgical activity recognition and surgical skill evaluation). Furthermore, the models provided in this study can potentially benefit the clinical community through standardization of the surgery, which is essential for surgical training.

Movement-level process modeling of microsurgical bimanual and unimanual tasks

PurposeMicrosurgical techniques require highly skilled manual handling of specialized surgical instruments. Surgical process models are central for objective evaluation of these skills, enabling data-driven solutions that can improve intraoperative efficiency.MethodWe built a surgical process model, defined at movement level in terms of elementary surgical actions (n=4) and targets (n=4). The model also included nonproductive movements, which enabled us to evaluate suturing efficiency and bi-manual dexterity. The elementary activities were used to investigate differences between novice (n=5) and expert surgeons (n=5) by comparing the cosine similarity of vector representations of a microsurgical suturing training task and its different segments.ResultsBased on our model, the experts were significantly more efficient than the novices at using their tools individually and simultaneously. At suture level, the experts were significantly more efficient at using their left hand tool, but the differences were not significant for the right hand tool. At the level of individual suture segments, the experts had on average 21.0 % higher suturing efficiency and 48.2 % higher bi-manual efficiency, and the results varied between segments. Similarity of the manual actions showed that expert and novice surgeons could be distinguished by their movement patterns.ConclusionsThe surgical process model allowed us to identify differences between novices’ and experts’ movements and to evaluate their uni- and bi-manual tool use efficiency. Analyzing surgical tasks in this manner could be used to evaluate surgical skill and help surgical trainees detect problems in their performance computationally.

Unsupervised Identification of Surgical Robotic Actions From Small Non-Homogeneous Datasets

Robot-assisted surgery is an established clinical practice. The automatic identification of surgical actions is needed for a range of applications, including performance assessment of trainees and surgical process modeling for autonomous execution and monitoring. However, supervised action identification is not feasible, due to the burden of manually annotating recordings of potentially complex and long surgical executions. Moreover, often few example executions of a surgical procedure can be recorded. This letter proposes a novel fast algorithm for unsupervised identification of surgical actions in a standard surgical training task, the ring transfer, executed with da Vinci Research Kit. Exploiting kinematic and semantic visual features automatically extracted from a very limited dataset of executions, we are able to significantly outperform state-of-the-art results on a dataset of non-expert executions (58% vs. 24% F1-score), and improve performance in the presence of noise, short actions and non-homogeneous workflows, i.e. non repetitive action sequences.

Simulation-to-real domain adaptation with teacher\u2013student learning for endoscopic instrument segmentation

PurposeSegmentation of surgical instruments in endoscopic video streams is essential for automated surgical scene understanding and process modeling. However, relying on fully supervised deep learning for this task is challenging because manual annotation occupies valuable time of the clinical experts.MethodsWe introduce a teacher–student learning approach that learns jointly from annotated simulation data and unlabeled real data to tackle the challenges in simulation-to-real unsupervised domain adaptation for endoscopic image segmentation.ResultsEmpirical results on three datasets highlight the effectiveness of the proposed framework over current approaches for the endoscopic instrument segmentation task. Additionally, we provide analysis of major factors affecting the performance on all datasets to highlight the strengths and failure modes of our approach.ConclusionsWe show that our proposed approach can successfully exploit the unlabeled real endoscopic video frames and improve generalization performance over pure simulation-based training and the previous state-of-the-art. This takes us one step closer to effective segmentation of surgical instrument in the annotation scarce setting.

Workflow assessment as a preclinical development tool : Surgical process models of three techniques for minimally invasive cochlear implantation.

Minimally invasive cochlear implant surgery is a challenging procedure due to high demands on accuracy. For clinical success, an according assistance system has to compete against the traditional approach in terms of risk, operating time and cost. It has not yet been determined what kind of system is the most suited. The purpose of this study is a proof of concept of surgical process modeling as a preclinical development tool and the comparison of workflow concepts for this new approach. Three preclinical systems (two stereotactic and one robotic) for minimally invasive cochlear implant surgery are compared using the method of surgical process modeling. All three systems were successfully tested with ex vivo human specimen to create minimally invasive surgical access to the cochlea. Those systems where chosen for comparison, because they represent three diverse approaches with different corresponding workflows for the same intervention. The experiments were used to create a process model for each system by recording the interventions. All three conceptual systems developed by our group have shown their eligibility. The recorded process models provide a convenient method for direct comparison. Reduction in the surgical time has a higher impact on the process, than time that is needed for setting up a system beforehand. The stereotactic approaches have little preparation effort and are low cost in terms of hardware compared to the robotic approach, which in return is beneficial in terms of workload reduction for the surgeon. Surgical process modeling is suitable for comparison of different assistant systems for minimally invasive cochlear implantation. The benefit of reduced trauma, compared to the traditional mastoidectomy, can now be assessed with consideration of the workflow of each technique. The process models enable an assessment in the regard of surgical time and workload.

Extending BPMN 2.0 for intraoperative workflow modeling with IEEE 11073 SDC for description and orchestration of interoperable, networked medical devices.

Surgical workflow management in integrated operating rooms (ORs) enables the implementation of novel computer-aided surgical assistance and new applications in process automation, situation awareness, and decision support. The context-sensitive configuration and orchestration of interoperable, networked medical devices is a prerequisite for an effective reduction in the surgeons' workload, by providing the right service and right information at the right time. The information about the surgical situation must be described as surgical process models and distributed to the medical devices and IT systems in the OR. Available modeling languages are not capable of describing surgical processes for this application. In this work, the BPMNSIX modeling language for intraoperative processes is technically enhanced and implemented for workflow build-time and run-time. Therefore, particular attention is given to the integration of the recently published IEEE 11073 SDC standard family for a service-oriented architecture of networked medical devices. In addition, interaction patterns for context-aware configuration and device orchestration were presented. The identified interaction patterns were implemented in BPMNSIX for an ophthalmologic use case. Therefore, the examples of the process-driven incorporation and control of device services could be demonstrated. The modeling of surgical procedures with BPMNSIX allows the implementation of context-sensitive surgical assistance functionalities and enables flexibility in terms of the orchestration of dynamically changing device ensembles and integration of unknown devices in the surgical workflow management.

Process-Model-Driven Guidance to Reduce Surgical Procedure Errors: An Expert Opinion

This paper explains how a detailed, precise surgical process model can help reduce errors by fostering better understanding, providing guidance during surgery, helping train newcomers, and by supporting process improvement. It describes the features that a process-modeling language should have in order to support the precise specification of such models.

Comparative analysis of surgical processes for image-guided endoscopic sinus surgery

This study proposes a method to analyze surgical performance by modeling, aligning, and comparing surgical processes. This method is intended to serve as a means to support the enhancement of surgical skills for endoscopic sinus surgeries (ESSs). We focus on surgical navigation systems used in image-guided ESSs and aim to construct a comparative analysis method for surgical processes based on the information about the surgical instruments motion obtained from the navigation system. The proposed method consists of the following three parts: quantification of surgical features, modeling of surgical processes, and alignment and comparison of surgical process models (SPMs). First, we defined time-series parameters using the navigation-based surgical data. Second, we created SPMs by applying the defined parameters and the relative positional information of the instruments to the patient's anatomy. Third, we constructed a method to align and compare SPMs based on dynamic time warping with barycenter averaging. The proposed method was validated on a dataset containing surgical data obtained by an optical tracking system from 14 clinical ESS cases. We evaluated the validity of the comparative analysis by aligning and comparing SPMs between experts and residents. The validation results suggested that the proposed method could achieve proper alignment of the SPMs and clarify the differences in surgical processes between experts and residents. We developed a method to enable a time-series comparative analysis of surgical processes based on the surgical data from the navigation system. This method can allow surgeons to identify differences between their procedures and reference procedures such as experts' procedures.

Ontology-Based Surgical Subtask Automation, Automating Blunt Dissection

Automation of surgical processes (SPs) is an utterly complex, yet highly demanded feature by medical experts. Currently, surgical tools with advanced sensory and diagnostic capabilities are only available. A major criticism towards the newly developed instruments that they are not fitting into the existing medical workflow often creating more annoyance than benefit for the surgeon. The first step in achieving streamlined integration of computer technologies is gaining a better understanding of the SP. Surgical ontologies provide a generic platform for describing elements of the surgical procedures. Surgical Process Models (SPMs) built on top of these ontologies have the potential to accurately represent the surgical workflow. SPMs provide the opportunity to use ontological terms as the basis of automation, allowing the developed algorithm to easily integrate into the surgical workflow, and to apply the automated SPMs wherever the linked ontological term appears in the workflow. In this work, as an example to this concept, the subtask level ontological term “blunt dissection” was targeted for automation. We implemented a computer vision-driven approach to demonstrate that automation on this task level is feasible. The algorithm was tested on an experimental silicone phantom as well as in several ex vivo environments. The implementation used the da Vinci surgical robot, controlled via the Da Vinci Research Kit (DVRK), relying on a shared code-base among the DVRK institutions. It is believed that developing and linking further building blocks of lower level surgical subtasks could lead to the introduction of automated soft tissue surgery. In the future, the building blocks could be individually unit tested, leading to incremental automation of the domain. This framework could potentially standardize surgical performance, eventually improving patient outcomes.

Toward a standard ontology of surgical process models.

The development of common ontologies has recently been identified as one of the key challenges in the emerging field of surgical data science (SDS). However, past and existing initiatives in the domain of surgery have mainly been focussing on individual groups and failed to achieve widespread international acceptance by the research community. To address this challenge, the authors of this paper launched a European initiative-OntoSPM Collaborative Action-with the goal of establishing a framework for joint development of ontologies in the field of SDS. This manuscript summarizes the goals and the current status of the international initiative. A workshop was organized in 2016, gathering the main European research groups having experience in developing and using ontologies in this domain. It led to the conclusion that a common ontology for surgical process models (SPM) was absolutely needed, and that the existing OntoSPM ontology could provide a good starting point toward the collaborative design and promotion of common, standard ontologies on SPM. The workshop led to the OntoSPM Collaborative Action-launched in mid-2016-with the objective to develop, maintain and promote the use of common ontologies of SPM relevant to the whole domain of SDS. The fundamental concept, the architecture, the management and curation of the common ontology have been established, making it ready for wider public use. The OntoSPM Collaborative Action has been in operation for 24months, with a growing dedicated membership. Its main result is a modular ontology, undergoing constant updates and extensions, based on the experts' suggestions. It remains an open collaborative action, which always welcomes new contributors and applications.

Development of an Interactive Dashboard to Analyze Cognitive Workload of Surgical Teams During Complex Procedural Care.

In the surgical setting, team members constantly deal with a high-demand operative environment that requires simultaneously processing a large amount of information. In certain situations, high demands imposed by surgical tasks and other sources may exceed team member's cognitive capacity, leading to cognitive overload which may place patient safety at risk. In the present study, we describe a novel approach to integrate an objective measure of team member's cognitive load with procedural, behavioral and contextual data from real-life cardiac surgeries. We used heart rate variability analysis, capturing data simultaneously from multiple team members (surgeon, anesthesiologist and perfusionist) in a real-time and unobtrusive manner. Using audio-video recordings, behavioral coding and a hierarchical surgical process model, we integrated multiple data sources to create an interactive surgical dashboard, enabling the analysis of the cognitive load imposed by specific steps, substeps and/or tasks. The described approach enables us to detect cognitive load fluctuations over time, under specific conditions (e.g. emergencies, teaching) and in situations that are prone to errors. This in-depth understanding of the relationship between cognitive load, task demands and error occurrence is essential for the development of cognitive support systems to recognize and mitigate errors during complex surgical care in the operating room.

Intelligent Interruption Management System to Enhance Safety and Performance in Complex Surgical and Robotic Procedures.

Procedural flow disruptions secondary to interruptions play a key role in error occurrence during complex medical procedures, mainly because they increase mental workload among team members, negatively impacting team performance and patient safety. Since certain types of interruptions are unavoidable, and consequently the need for multitasking is inherent to complex procedural care, this field can benefit from an intelligent system capable of identifying in which moment flow interference is appropriate without generating disruptions. In the present study we describe a novel approach for the identification of tasks imposing low cognitive load and tasks that demand high cognitive effort during real-life cardiac surgeries. We used heart rate variability analysis as an objective measure of cognitive load, capturing data in a real-time and unobtrusive manner from multiple team members (surgeon, anesthesiologist and perfusionist) simultaneously. Using audio-video recordings, behavioral coding and a hierarchical surgical process model, we integrated multiple data sources to create an interactive surgical dashboard, enabling the identification of specific steps, substeps and tasks that impose low cognitive load. An interruption management system can use these low demand situations to guide the surgical team in terms of the appropriateness of flow interruptions. The described approach also enables us to detect cognitive load fluctuations over time, under specific conditions (e.g. emergencies) or in situations that are prone to errors. An in-depth understanding of the relationship between cognitive overload states, task demands, and error occurrence will drive the development of cognitive supporting systems that recognize and mitigate errors efficiently and proactively during high complex procedures.